Category: BigData

In the couple of days left before the year end I wanted to look back and reflect on what has happened so far in the IT bubble 2.0 commonly referred to as “BigData”. Here are some of my musings.

Let’s start with this simple statement: BigData is misnomer. Most likely it has been put forward by some PR or MBA schmuck with no imagination whatsoever, who thought that terabyte consists of 1000 megabytes 😉 The word has been picked up by pointy-haired bosses all around the world as they need buzzwords to justify their existence to people around. But I digressed…

So what has happened in the last 12 months in this segment of software development? Well, surprisingly you can count real interesting events on one hand. To name a few:

Fault tolerance in the distributed systems got to the new level with NonStop Hadoop, introduced by WANdisco earlier this year. The idea of avoiding complex screw-ups by agreeing on the operations up-front is leaving things like Linux HA, Hadoop QJM, and NFS based solutions rolling in the dust in the rear-view mirror.

Hadoop HDFS is clearly here to stay: you can see customers shifting from platforms like Teradata towards cheaper and widely supported HDFS network storage; with EMC (VMWare, Greenplum, etc.) offering it as the storage layer under Greenplum’s proprietary PostegSQL cluster and many others.

While enjoying a huge head start, HDFS has a strong while not very obvious competitor – CEPH. As some know, there’s a patch that provides CEPH drop-in replacement for HDFS. But where it get real interesting is how systems like Spark (see next paragraph) can work directly on top of CEPH file-system with a relatively small changes in the code. Just picture it:

distributed Linux file-system high-speed data analytic

Drawing conclusions is left as an exercise to the readers.

With the recent advent and fast rise of new in memory analytic platform – Apache Spark (incubating) – the traditional, two bit, MapReduce paradigm is loosing the grasp very quickly. The gap is getting wider with new generation of the task and resource schedulers gaining momentum by the day: Mesos, Spark standalone scheduler, Sparrow. The latter is especially interesting with its 5ms scheduling guarantees. That leaves the latest reincarnation of the MR in the predicament.

Shark – SQL layer on top of Spark – is winning the day in the BI world, as you can see it gaining more popularity. It seems to have nowhere to go but up, as things like Impala, Tez, ASF Drill are still very far away from being accepted in the data-centers.

With all above it is very exciting to see my good friends from AMPlab spinning up a new company that will be focusing on the core platform of Spark, Shark and all things related. All best wishes to Databricks in the coming year!

Speaking of BI, it is interesting to see that Bigdata BI and BA companies are still trying to prove their business model and make it self-sustainable. The case in point, Datameer with recent $19M D-round; Platfora’s last year $20M B-round, etc. I reckon we’ll see more fund-raisers in the 107 or perhaps 108 of dollars in the coming year among the application companies and platform ones. Also new letters will be added to the mix: F-rounds, G-rounds, etc. as cheap currency keeps finding its way from the Fed through the financial sector to the pockets of VCs and further down to high-risk sectors like IT and software development. This will lead to over-heated job market in the Silicon Valley and elsewhere followed by a blow-up similar to but bigger than 2000-2001. It will be particularly fascinating to watch big companies scavenging the pieces after the explosion. So duck to avoid shrapnel.

Stack integration and validation has became a pain-point for many. And I see the effects of it in shark uptake of the interest and growth of Apache Bigtop community. Which is no surprise, considering that all commercial distributions of Hadoop today are based or directly using Bigtop as the stack producing framework.

While I don’t have a crystal ball (would be handy sometimes) I think a couple of very strong trends are emerging in this segment of the technology:

HDFS availability – and software stack availability in general – is a big deal: with more and more companies adding HDFS layer into their storage stack more strict SLAs will emerge. And I am not talking about 5 nines – an equivalent of 5 minutes downtime per year – but rather about 6 and 7 nines. I think Zookeeper based solutions are in for a rough ride.

Machine Learning has a huge momentum. Spark summit was a one big evidence of it. With this comes the need to incredibly fast scheduling and hardware utilization. Hence things like Mesos, Spark standalone and Sparrow are going to keep gaining the momentum.

Seasonal lemming-like migration to the cloud will continue, I am afraid. The security will become a red-hot issue and an investment opportunity. However, anyone who values their data is unlikely to move to the public cloud, hence – private platforms like OpenStack might be on the rise (if the providers can deal with “design by committee” issues of course).

Storage and analytic stack deployment and orchestration will be more pressing than ever (no, I am talking about real orchestration, not cluster management software). That’s why I am looking very closely on that companies like Reactor8 are doing in this space.

So, last year brought a lot of excitement and interesting challenges. 2014, I am sure, will be even more fun. However “living in the interesting times” might a curse and a blessing. Stay safe, my friends!

Skimming through my emails today I have came across this interesting post on general@hadoop list:

From MTG dev

Subject

Lightning fast in-memory analytics on HDFS

Date

Mon, 24 Sep 2012 16:31:56 GMT

Because a lot of people here are using HDFS day in and day out thefollowing might be quite interesting for some.

Magna Tempus Group has just rolled out a readily available Spark 0.5(www.spark-project.org) packaged for Ubuntu distribution. Spark delivers upto 20x faster experience (sic!) using in-memory analytics and a computationalmodel that is different from MapReduce.

You can read the rest here. If you don’t know about Spark then you sure should check the Spark project website and see how cool is that. If you are lazy to dig through the information, here’s a brief summary for you (taken from the original poster’s Magna Tempus Group website)

consists of a completely separate codebase optimized for low latency, although it can load data from any Hadoop input source, S3, etc.

doesn’t have to use Hadoop, actually

provides a new, highly efficient computational model, with programming interfaces in Scala, Java. We might start working soon on adding Groovy API to the set

offers a lazy evaluation that allows a “postponed” execution of operations

can do in-memory caching of data for later high-performance analytics. Yeah, go shopping for more RAM, gents!

can be run locally on a multicore system or on a Mesos cluster

Yawn, some might say. There are Apache Drill and other things that seems to be highly promising and all. Well, not so fast.

To begin with, I am not aware about any productized version of Drill (merged with Open Dremel or vice versa). Perhaps, there are some other technologies around that are 20x faster than Hadoop – I just haven’t heard about them, so please feel free to correct me on this.

Also, Spark and some of its components (Mesos resource planner and such) have been happily adopted by interesting companies such as Twitter and so on.

What is not said out right is that an adoption of new in-memory high-performance analytics for big data by commercial vendors like Magna Tempus Group opens a completely new page in the BigData storybook.

I would “dare” to go as far as to assert that this new development means that Hadoop isn’t the smartest kid on the block anymore – there are other faster and perhaps clever fellas moving in.

And I can’t help but wonder if the Spark has lit a fire under the yellow elephant yet?

With more and more people jumping on bandwagon of big data it is very settling to see that Hadoop is gaining momentum by a day.

Even most fascinating is too see how the idea of putting together a bunch of service components on top of Hadoop proper is getting more and more momentum. IT and software development professionals are getting better understanding about benefits that a flexible set of loosely coupled yet compatible components provides when one needs to customize data processing solution at scale.

The biggest problem for most businesses trying to add Hadoop infrastructure into their existing IT is a lack of knowledge, professional support, and/or clear understanding of what’s out there on the market to help you. Essentially, Hadoop exists in one incarnation – this is the open-source project under the umbrella of Apache Software Foundation (ASF). This is where all the innovations in Hadoop are coming from. And essentially this is a source of profit for a few commercial offerings today.

What’s wrong with the picture, you might ask? Well, the main issue with most of these “commercial offerings” are mostly two folds. They are either immature and based on an sometimes unfinished nor unreleased Hadoop code, or provide no significant value add compare to Hadoop proper available in source form from hadoop.apache.org. And no matter if any of above (or both of them together) apply to a commercial solution based on Hadoop, you can be sure of one thing: these solutions will cost you literally tons of money – as much as $1k/node/year in some cases – for what is essentially available for free.

“What about neat packages I can get from a commercial provider and perhaps some training too?” one might ask. Well, yeah if you are willing to pay top bucks per node for say like this to get fixed or learn how to install packages on a virtual machine – go ahead by all means.

However, keep in mind that you always can get a set of packages for Hadoop produced by another open source project called Bigtop, hosted by Apache. What essentially you get are packages for your Linux distro, which can be easily installed on your cluster’s nodes. A great benefit is that you can easily trim your Hadoop stack to only include what you need: Hadoop + Hive, or perhaps Hadoop + HBase (which will automatically pick up Zookeper for you).

At any rate, the best part of the story isn’t a set of packages that can be installed: after all this is what packages are usually being created for, right? The problem with the packages or other forms of component distribution is that you don’t know in advance if A-package will nicely work with B-package v.1.2 unless some has tested this assumption before. Even then, testing environment might be significantly different from your production environment and then all bets are off. Unless – again – you’re willing to pay through your nose to someone who’s willing to get it for you. And that’s where true miracle of something like BigTop is coming for a rescue.

Before I’ll explain more, I wanna step back a bit and take a look at some recent history. A couple of years ago Yahoo’s Hadoop development team had to address an issue of putting together working and well-validated Hadoop stack including a number of components developed by different engineering organizations with their own development schedule and integration criteria. The main integration point of all of the pieces was the operations team which was in charge of big number of cluster deployments, provisioning and support. Without their own QA staff they were oftentimes at mercy of assumed code or configuration quality coming from all the corners of the company. Yet worst, even with a chance of the high quality of all these components there were no guarantees that they will work together as expected once put together on the cluster. And indeed, integration problems were many.

That’s were a small team of engineers including yours truly put together a prototype of a system called FIT (Final Integration Testing). The system essentially allowed you to pick up a packaged component you want to validate against your cluster environment and perform the deployment, configuration, and testing with integration scenarios provided by either component’s owner or your own team.

The approach was so effective that the project was continued and funded further in the form of HIT (Hadoop Integration Testing). At which point two of us have left for what seemed like a greener pasture back then 😦

We thought the idea was real promising so we have continued on the path of developing a less custom and more adoptable technology based on open standards such as Maven and Groovy. Here you can find slides from the talk we gave at eBay about a year ago. The presentation is putting the concept of Hadoop data stack in open writing for the time, as well as stacks customization and validation technology. When this presentation were given we already had well working mechanism of creating, deploying, and validating both packaged and non-packaged Hadoop components.

BigTop – open-sourced for the second time just a few months and based on our project above – has added up a packaging creation layer on top of the stack validation product. This, of course, makes your life even easier. And even more so with a number of Puppet recipes allowing you to deploy and configure your cluster in highly efficient and automatic manner. I encourage you to check it out.

BigTop has been successfully used for validating release of Apache Hadoop 0.20.205 which has become a foundation of coming Hadoop 1.0.0 Another release of Hadoop – 0.22 – was using BigTop for release candidates validation and so on.